WiMi Hologram Cloud is researching neural network models to optimize parameter selection in twin-field quantum key distribution (TF-QKD) systems. Three models were evaluated: BPNN, RBFNN, and GRNN. RBFNN and GRNN showed higher prediction accuracy in high-dimensional parameter spaces, while BPNN offered the fastest computation speed. Compared to traditional local search algorithms (LSA), neural network-based prediction reduced computation time by multiple orders of magnitude. Future work will explore deep learning and reinforcement learning approaches, with plans to integrate the technology into quantum communication hardware for practical deployment.
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